Finding Precise Causal Multi-Drug-Drug Interactions for Adverse Drug Reaction Analysis

ABSTRACT

Mechanisms are provided for implementing a framework to learn multiple drug-adverse drug reaction associations. The mechanisms receive and analyze patient electronic medical record data and adverse drug reaction data to identify co-occurrences of references to drugs with references to adverse drug reactions (ADRs) to thereby generate candidate rules specifying multiple drug-ADR relationships. The mechanisms filter the candidate rules to remove a subset of one or more rules having confounder drugs specified in the subset of one or more candidate rules, and thereby generate a filtered set of candidate rules. The mechanisms further generate a causal model based on the filtered set of candidate rules. The causal model comprises, for each ADR in a set of ADRs, a corresponding set of one or more rules, each rule specifying a combination of drugs having a causal relationship with the ADR.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for findingprecise casual multi-drug-drug interactions for adverse drug reactionanalysis.

An adverse drug reaction (ADR) is an injury caused by taking amedication or drug. ADRs may occur following a single dose or prolongedadministration of a drug. ADRs may be classified by severity and cause.ADRs may be local or systemic. The study of ADRs is the concern of thefield known as pharmacovigilance.

Causality assessment is used to determine the likelihood that a drugcaused a suspected ADR. There are a number of different methods used tojudge causation, including the Naranjo algorithm, the Venulet algorithm,and the World Health Organization (WHO) causality term assessmentcriteria. Each have pros and cons associated with their use and mostrequire some level of expert judgment to apply. However, assigningcausality to a specific agent often proves difficult, unless the eventis found during a clinical study or large databases are used. Bothmethods have difficulties and can be fraught with error. Even inclinical studies, some ADRs may be missed as large numbers of testindividuals are required to find the ADR.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system comprising at least one processor and at least onememory, the at least one memory comprising instructions executed by theat least one processor to cause the at least one processor to implementa framework for learning multiple drug-adverse drug reactionassociations. The method comprises receiving, by the framework, realworld evidence comprising patient electronic medical record data andadverse drug reaction data and analyzing, by a co-occurrence logicmodule of the framework, the real world evidence to identifyco-occurrences of references to drugs with references to adverse drugreactions (ADRs) to thereby generate candidate rules specifying multipledrug-ADR relationships. The method further comprises filtering, by aconfounder filter logic module of the framework, the candidate rules toremove a subset of one or more rules having confounder drugs specifiedin the subset of one or more candidate rules, and thereby generate afiltered set of candidate rules. Moreover, the method comprisesgenerating, by a causal association logic module of the framework, acausal model based on the filtered set of candidate rules, wherein thecausal model comprises, for each ADR in a set of ADRs, a correspondingset of one or more rules, each rule specifying a combination of drugshaving a causal relationship with the ADR.

In some illustrative embodiments, analyzing the real world evidenceincludes identifying all co-occurrences of references to drugs withreferences to ADRs in the real world evidence and generating thecandidate rules based on the identified co-occurrences, where eachcandidate rule specifies a drug pattern identifying a plurality ofdrugs, and a corresponding ADR. Moreover, in some illustrativeembodiments, analyzing the real world evidence further includesselecting a sub-set of the candidate rules as a basis for generating thecandidate multiple drug-ADR relationships based on at least one of asupport metric and a confidence metric, where the support metricmeasures a number of instances of the co-occurrence, corresponding tothe candidate rule, in the real world evidence, and where the confidencemetric measures, for a candidate rule, a probability of the ADR giventhe drugs in the drug pattern of the candidate rule.

In some illustrative embodiments, identifying all co-occurrences ofreferences to drugs with reference to ADRs in the real world evidenceincludes performing natural language processing of the real worldevidence to identify at least one of terms, phrases, or medical codesidentifying references to drugs and references to ADRs, evaluatingrelative distances within the real world evidence, between eachidentified term, phrase or medical code identifying references to drugsand references to ADRs, and identifying co-occurrences based on therelative distances.

In some illustrative embodiments, selecting a sub-set of candidate rulesincludes, for each candidate rule, generating a contingency table datastructure, where each entry in the contingency table data structurecomprises a number of patient electronic medical records, that satisfy acondition of the row and column of the contingency table data structurecorresponding to the entry. In the contingency table data structure, afirst row of the contingency table data structure corresponds to patientelectronic medical records that contain all drugs in the drug pattern ofthe candidate rule, a second row of the contingency table data structurecorresponds to patient electronic medical records that contain none ofthe drugs in the drug pattern of the candidate rule, a first column ofthe contingency table data structure corresponds to the patientelectronic medical records that contain the ADR in the candidate rule,and a second column of the contingency table data structure correspondsto the patient electronic medical records that do not contain the ADR inthe candidate rule.

In still other illustrative embodiments, each candidate rule specifies acorresponding drug pattern and corresponding adverse drug reaction, andfiltering the candidate rules further includes calculating, for eachfirst candidate rule, an improvement metric specifying an amount ofimprovement of a corresponding association score for the first candidaterule over an association score for another second candidate rulespecifying a sub-pattern of the corresponding drug pattern of the firstcandidate rule, and the corresponding adverse drug reaction of the firstcandidate rule. Moreover, filtering the candidate rules may furtherinclude determining, for each first candidate rule, whether to maintainthe first candidate rule or remove the first candidate rule based on avalue of the improvement metric.

In some illustrative embodiments, determining, for each first candidaterule, whether to maintain the candidate rule or remove the candidaterule based on a value of the improvement metric includes comparing theimprovement metric corresponding to the first candidate rule to animprovement metric threshold value, and in response to the improvementmetric corresponding to the first candidate rule not being equal to orgreater than the improvement metric threshold value, determining that aconfounder drug is present in the corresponding first drug pattern.Moreover, in some illustrative embodiments, determining, for each firstcandidate rule, whether to maintain the first candidate rule or removethe first candidate rule based on the value of the improvement metricfurther includes identifying the confounder drug in the correspondingdrug pattern based on a difference between the corresponding drugpattern and the sub-pattern.

In other illustrative embodiments, the method further includesevaluating other patient electronic medical record data by applying thecausal model to drug history data present in the other patientelectronic medical record data to identify probabilities of a patientencountering one or more ADRs in the set of ADRs. Moreover, in someillustrative embodiments, the method also includes generating, for thepatient, a patient model based on the identified probabilities of thepatient encountering one or more ADRs in the set of ADRs. Furthermore,in some illustrative embodiments, the method comprises inputting thepatient model into a cognitive system which implements the patient modelto perform a cognitive operation based on the patient model. In someillustrative embodiments, the cognitive operation is a treatmentrecommendation operation that provides a treatment recommendation to amedical practitioner based on an evaluation of the other patientelectronic medical record data by the cognitive system, and the patientmodel.

In other illustrative embodiments, a computer program product comprisinga computer usable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example flow diagram illustrating an overall representationof the methodology employed by the mechanisms of one illustrativeembodiment;

FIG. 2 is an example diagram of a contingency table in accordance withone illustrative embodiment;

FIG. 3 is an example diagram of a distributed data processing systemenvironment in which aspects of one illustrative embodiment may beimplemented;

FIG. 4 is an example block diagram of a data processing system in whichaspects of the present invention may be implemented in accordance withone illustrative embodiment;

FIG. 5 illustrates an interaction of a multiple drug pattern/ADRassociation learning framework with a cognitive healthcare system inaccordance with one illustrative embodiment;

FIG. 6 illustrates an example of an interaction between a multiple drugpattern/ADR association learning framework with a question answering orrequest processing pipeline in accordance with one illustrativeembodiment; and

FIG. 7 is a flowchart outlining an example operation for learningmultiple drug pattern relationships with adverse drug reactions andfiltering out confounder drugs in accordance with one illustrativeembodiment.

DETAILED DESCRIPTION

The strengths of current cognitive systems, such as current medicaldiagnosis, patient health management, patient treatment recommendationsystems, and other decision support systems, are that they can provideinsights that improve the decision making performed by human beings. Forexample, in the medical context, such cognitive systems may improvemedical practitioners' diagnostic hypotheses, can help medicalpractitioners avoid missing important diagnoses, and can assist medicalpractitioners with determining appropriate treatments for specificdiseases. However, current systems still suffer from significantdrawbacks which should be addressed in order to make such systems moreaccurate and usable for a variety of applications as well as morerepresentative of the way in which human beings make decisions, such asdiagnosing and treating patients. In particular, one drawback of currentsystems is the ability to learn and take into consideration drug-druginteractions and determining causality with regard to adverse drugreactions (ADRs) when patients are taking multiple drugs.

That is, patients often take multiple drugs contemporaneously. This maybe due to the patient having multiple different conditions for whichdifferent drugs are being taken to address each of the conditions, maybe due to a treatment regimen requiring multiple drugs for treatment ofa medical condition, or any of a number of other reasons. The patient'staking of multiple drugs may, or may not, be known by an individualmedical practitioner when treating the patient. That is, if the patientis being treated by different medical practitioners for differentmedical conditions, one medical practitioner may not be aware of drugsprescribed by the other practitioner if that information is not presentin the patient's local medical file and/or is not reported by thepatient to the medical practitioner.

One of the key ways to treat various medical conditions, e.g., diseasessuch as diabetes, cancer, etc., is to place the patient on a treatmentregimen that requires the administering of multiple medications, drugs,supplements, or the like (referred to collectively herein as “drugs”).For example, in treating Diabetes Type II, a first line treatment istypically a single drug while a second line treatment may comprisemultiple drugs, e.g., metformin and another drug that may depend on theparticular patient condition. Even though precautions are taken to avoidnegative interactions of drugs, medical personnel are not always awareof all of the possible negative interactions. Moreover, there may beinteractions based on the patient's particular attributes andcomorbidities that make using treatments involving multiple drugs anissue, which may not be readily apparent to the medical personnel.Furthermore, patients often have more than one medical condition, e.g.,chronic diseases, and may be on different drugs for the differentmedical conditions, leading to additional potential for negativeinteractions of drugs.

Drug-Drug interactions are serious threats that can result insignificant morbidity and mortality, causing nearly 74,000 emergencyroom visits and 195,000 hospitalizations each year in the United Statesof America. The illustrative embodiments set forth herein providemechanisms to identify causal multiple drug-drug interactions responsivefor adverse drug reactions (ADRs) from real world evidence. Theillustrative embodiments utilize an association rule mining approach foreffectively identifying higher dimensional or higher order drug-drugassociations from observational data. Statistical scores are used toidentify the precise drug-drug interactions to filter out confounders.Furthermore, obtained rules (both higher-order and singleton drugs)related to a particular ADR are input into a Bayesian framework forbuilding a causal discovery framework. The illustrative embodimentsinterpret the obtained parameters of the Bayesian framework for causaldiscovery. This causal discovery may be used by cognitive systems toassist medical personnel in evaluating and treating patients byproviding additional information regarding the causal relationshipsbetween drugs being taken by patients and their potential for causingadverse drug reactions whether alone or in combination with other drugsthat the patient may be taking. Moreover, the causal discovery assistsmedical personnel in eliminating potential sources of ADRs as simplyconfounders, thereby avoiding erroneous diagnosis and/or erroneousevaluation of the causes of ADRs.

The illustrative embodiments provide significant advantages overprevious approaches to finding ADRs associated with drugs. Previousapproaches to finding ADRs associated with drugs mainly focus on findingsimple co-occurrences of drugs with ADRs from observational data andspontaneous reporting systems. For example, Harpaz et al., “MiningMulti-Item Drug Adverse Effect Associations in Spontaneous ReportingSystems,” BMC Bioinformatics, Oct. 28, 2010; 11(9):S7describes anapproach that applies a generic association rule mining framework forfinding associations based on simple co-occurrences of drugs. As anotherexample, Xiang et al., “Efficiently Mining Adverse Event ReportingSystem for Multiple Drug Interactions,” AMIA Summits on TranslationalScience Proceedings, 2014; 2014:120 describes a methodology that alsouses an association rule mining framework for finding minimalrepresentations of drug-drug interactions. In yet another example, Du etal., “Graphic Mining of High-Order Drug Interactions and TheirDirectional Effects on Myopathy Using Electronic Medical Records,” CPT:Pharmacometrics & Systems Pharmacology, Aug. 1, 2015; 4(8):481-8describes a statistical approach for finding drug interactions formyopathy.

The illustrative embodiments, on the other hand, provide mechanismswhich exploit the higher order associations of the drug-drug interactionfrom electronic health records. Moreover, the mechanisms of theillustrative embodiments remove the drugs, from the multi-drugassociations, that are simple confounders that are co-prescribed forco-morbidities. In addition, the mechanisms of the illustrativeembodiments may be used to predict ADRs (one or more) that areassociated with multi-drug associations of particular patients as may beidentified by a cognitive analysis of the patients' electronic medicalrecords (EMRs).

The mechanisms of the illustrative embodiments operate based on theobservation that ADRs may be caused by interactions among multiple drugsrather than caused by a single drug. The known approaches noted abovehave limitations in that they cannot find causal higher-order, i.e.higher dimension, interactions among multiple drugs and do not provide agenerally applicable approach to finding such higher order multipledrug-drug interactions with corresponding ADRs. The term “higher order”as used herein refers to having two or more drugs as the casual factorto lead to one or more ADRs, which is denoted by a rule, e.g., D₁D₂→ADR₁or D₁D₂D₃→ADR₁ADR₂, where D_(x) is a drug and ADR_(y) is an adverse drugreaction, and → represents a causal relationship of the rule. The priorart approaches based on simple co-occurrences of drugs with ADRs do notnecessarily provide a causal relationship due to the prior artapproaches finding only simple co-occurrences of a drug with an ADR andnot being able to identify actual causal relationships. Moreover, suchprior art approaches cannot differentiate drugs that have actual causalrelationships from drugs that are simply co-prescribed due to aco-morbidity of the patient. Such co-prescribed drugs can act as mereconfounders rather than a causal factor of ADRs. The illustrativeembodiments provide mechanisms that determine actual causalrelationships of multiple drugs with ADRs, eliminating confounders.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general-purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine-readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As discussed above, finding multi-drug-drug interactions responsible forADRs using observational data is useful for finding real world evidence.Moreover, it has been determined that some drugs in multi-drugsituations can be a simple confounder prescribed together with otherdrugs for comorbidities rather than real causal interactions with otherdrugs. A confounder drug is an extraneous drug that, in a statisticalmodel, correlates (directly or inversely) with both the dependent drugvariable and the independent drug variable in the model. Thus, somedrugs may not actually contribute to particular ADRs and may not have anactual causal interaction with the ADRs.

The illustrative embodiments described herein leverage data miningalgorithms, such as association rule mining with Bayesian models, forexample, for finding the real causal interaction among multiple drugswith the ADRs from real world evidence data. In so doing, theillustrative embodiments identify multi-drug interactions responsiblefor ADRs as well as the confounders. Thus, the illustrative embodimentsprovide a computerized solution to finding causal relationships betweenmultiple drugs and ADRs present in patient EMR data, as well ascomputerized solutions for eliminating confounder drugs from suchrelationships. The mechanisms of the illustrative embodiments arespecifically directed to a computerized tool that performs suchoperations and thereby causes a computing system to perform cognitiveoperations that approximate human thought processes with regard toevaluating patient EMR data and determining causal relationships betweenmultiple drugs and ADRs. While this computing tool approximates thishuman thought process, it does so using operations that are onlyperformed by computing devices specifically configured to perform theoperations of the illustrative embodiments, which differ from theprocesses a human mind employs due to the nature of the computingenvironment.

The illustrative embodiments provide a general approach to findinghigher-order associations for multiple drugs with ADRs that will behelpful for finding rare ADRs that are only caused by interactions amongdrugs, which can be leveraged to prescribe safe drugs for patients withcomorbidities. The illustrative embodiments may also determine whichdrugs in a multi-drug combination are only confounders instead ofcontributing to an interaction and ADR. This will assist with targetrisk management strategies, for example a drug label change couldspecify which drugs to avoid in combination rather than withholding thedrug from all patients who happen to take the confounding drug.

FIG. 1 is an example flow diagram illustrating an overall representationof the methodology employed by the mechanisms of one illustrativeembodiment. The methodology employs three main computerized logicmodules 120-140 which operate on real word evidence (RWE) which maycomprise various types of data obtained from various different sourcesof information. For example, the RWE 110 may comprise patient electronicmedical records (EMRs), health insurance claims data, medication historyinformation (such as may be obtained from a pharmacy computing system,health insurance records, physician's computing system, and/or thelike), etc.

The computerized logic modules 120-140 may be implemented in speciallyconfigured computer hardware, software executed on computer hardwarewhich thereby causes the hardware to perform operations the hardware didnot perform prior to being configured with the software for execution,or any combination of specialized configured computer hardware andsoftware executed on computer hardware. It should be appreciated thatthe loading of software into a memory of the computing device or dataprocessing system, with subsequent execution of this software by one ormore processors of the computing device, data processing system,specifically configures the computing device or data processing systemto implement the specific computerized solution provided by theillustrative embodiments of the present invention. The computerizedsolution is a solution for providing computer aided evaluation ofpatient electronic medical record data, adverse drug reaction data, andother resource data, to identify real causal relationships betweenmultiple drugs and a corresponding adverse drug reaction, withouterroneously including confounder drugs, which previously was not able tobe performed. The problems with prior art solutions as discussed hereinlie in the reliance on mere co-occurrence of a reference to a drug and areference to an ADR as evidence of a causal relationship, which may notin fact actually exist. Thus, the hardware/software mechanisms of theillustrative embodiments provide a computer solution to a computer basedproblem.

As shown in FIG. 1, in one illustrative embodiment, the computerizedlogic modules 120-140 comprise co-occurrence logic module 120,confounder filter logic module 130, and causal association logic module140. The co-occurrence logic module 120 operates on the RWE 110 andgenerates co-occurrence output data that is provided to the confounderfilter logic module 130. The co-occurrence output data specifies theco-occurrences of drugs and ADRs found in the RWE 110. The confounderfilter logic module 130 evaluates this co-occurrence output data tofilter out instances of co-occurrences of drugs and ADRs which actuallyrepresent confounders and not potential causalities between those drugsand the ADRs. As discussed hereafter, this may be done using animprovement score mechanism for filtering such co-occurrences based onimprovement score and one or more established thresholds. The output ofthe confounder filter logic module 130 is provided to the causalassociation logic module 140 which builds a causality model 145, such asa Bayesian network model or the like. The causality model 145 may beapplied by various cognitive system logic 150-170 to identifyinteractions of multiple drugs with ADRs 150, identify commonconfounders 160, and predict ADRs for patients 170.

The logic modules 120-140 define a data mining framework 100 thatidentifies multi-drug interactions that are responsible for a set ofADRs. In general, these logic modules first perform association rulemining, identify and remove confounder drugs, and generate a causalmodel having causal rules between multiple drugs and corresponding ADRsor ADR groups based on the remaining association rule relationshipshaving removed the confounder drugs. As shown in FIG. 1, the framework100 utilizes the co-occurrence logic module 120 to process the RWE data110 and identify all possible combinations of drugs and ADRs that occurmore frequently in the RWE 110 data. “More frequently” may be evaluatedbased on a predetermined threshold or other predefined criteria set foridentifying combinations of drugs and ADRs that should be considered aspotential causal relationships. For example, one or more thresholds maybe set using a technique based on two metrics of association analysis,where these two metrics are called support and confidence. The supportmetric, or support score, measures the number of instances of theco-occurrences, or a number of instances of the particular rule, i.e.both the drugs and ADRs are present, while the confidence metric, orconfidence score, measures the probability of having the ADRs given thedrugs. Thereafter, a threshold is applied to both of these metrics orscores, e.g., support scores equal to or above 70% and confidence scoresequal to or above 85%, to get a set of rules as the “frequent” rules.

The co-occurrence logic module 120 may maintain a data structure tostore these two metrics (Support and Confidence metrics) of eachassociation rule associating a combination of two or more drugs with anADR or ADR group. All possible combination of drugs and ADRs aresearched systematically using this data structure for bettercomputational efficiency, since it requires a combinatorial search overthe drugs and ADRs as potential candidate of rules. More specifically,the co-occurrence logic module 120 may start with a simple rule with onedrug and one ADR to obtain the two metrics for such rules, and thenexplore higher-order rules using this metrics data structure. Ananti-monotonic property called a-priori rule is applied to filter outnon-interesting higher-order rules from the data.

For example, drugs A, B, and C may be found in the RWE 110 data, whichagain may comprise patient medication history information in associationwith identifications of ADRs, in patient EMR data, for example, wherethe patient EMR data may be EMR data for a plurality of patients. Hence,various instances of combinations of drugs A, B, and C may be alsoidentified as being associated with the identification of an ADR, e.g.,the adverse drug reaction P in the depicted example case. For example,there may be indications that a combination of A and B are associatedwith the ADR in the content of the patient EMR. Moreover, there may beindications that a combination of A and C are also associated with theADR, as well as A, B, and C in the patient EMR.

The co-occurrence logic module 120 may utilize various resourceinformation sources 125 to perform natural language processing ofstructured and unstructured (e.g., natural language document) data toidentify key terms, phrases, medical codes, and the like, that areindicative of particular drugs and adverse drug reactions. The resourceinformation sources 125 may comprise various types of resourceinformation that can be used as a basis for identifying portions ofcontent in the RWE 110 data that correspond to drugs and adversereactions. For example, the resource information may include naturallanguage medical journal data, medical guidelines document data, druglabel data, known adverse drug reaction (ADR) data associated with drugdata, drug interaction data, and the like. As an example, informationregarding drug-drug interactions are often found in natural languagedocumentation such as clinical statements, guidelines, and in somepatient statements. Drug-drug interaction information may also beprovided by drug manufacturers, health organizations, governmentalorganizations, and other sources in various forms. One example of asource of drug information that includes drug-drug interactioninformation, is the Gold Standard Drug Database, available fromElsevier. Moreover, domain specific dictionaries of terms and phrases,medical code reference documents, and the like, may be used to specifyidentifiable terms, phrases, and medical codes that may be identified incontent of the RWE 110 data.

The co-occurrence logic module 120 may analyze the relative distance(e.g., in the same entry, within a certain number of words, within acertain number of entries, etc.) between instances of identifiers ofdrugs and instances of terms/phrases/medical codes, etc. indicative ofan ADR to determine associations, e.g., if the drug instance in thepatient EMR is present within a same encounter entry as the indicator ofthe ADR, then there may be an association. Moreover, the co-occurrencelogic module 120 may also look to temporal information present in theRWE 110 data to determine whether there is a co-occurrence of aninstance of a drug with an instance of another drug and with an ADR. Forexample, if the patient is prescribed a drug and an instance of an ADRis recorded within a specified time period after the prescribing of thedrug, then a co-occurrence may be determined to exist. Similarly, suchtemporal determinations may be made with regard to prescribing multipledrugs. Moreover, the duration of time over which the patient is supposedto be taking the drugs that they are prescribed may be evaluated todetermine if more than one drug is likely being taken by the patient atapproximately the same time, and approximately the same time as areported ADR. In addition, natural language processing may be performedon entries in the patient EMR data to identify terms/phrases indicativeof associations, such as terms like “caused” or “results in”, or thelike. Various distance based, temporal based, and/or natural languageprocessing-based evaluations of the RWE 110 data may be performed by theco-occurrence logic module 120, using the resource information from theresource information sources, to identify co-occurrences of drugs withother drugs and with ADRs.

Another source of information for finding drug-ADR associations is thepublicly available post-marketing surveillance data obtained fromseveral drug monitoring organizations, such as the Food and DrugAdministration (FDA) and Side Effect Resource (SIDER). In particular,these types of post-marketing data are created based on the reportedADRs of a certain drug from patients or medical providers after thepatient has been using the drug for a certain period of time. Such casereports are well-validated and documented by the FDA. Similarly, anothersource of such publicly available dataset can be the SIDER dataset,which is crafted from side-effects reported in the drug labels usingCurrent Procedural Terminology (CPT) codes.

The evaluations of these various factors for identifying co-occurrencesof drugs with other drugs and with ADRs may be used to calculateassociation scores for various combinations and associations of drugsand ADRs. That is, the co-occurrence logic module 120 may calculate, foreach combination of drugs determined to have co-occurrence in the RWE110, an association score representing a strength of the associationbetween that particular combination or pattern or drugs and theco-occurring ADR found in the RWE 110. Put another way, theco-occurrences may be represented as candidate rules specifying a drugpattern identifying a plurality of drugs, which have a candidate causalrelationship with an ADR. Thus, for a candidate rule ABC→P, for example,corresponding to a co-occurrence of a drug pattern ABC and an adversedrug reaction P, the association score represents the probability orlikelihood that P will occur given ABC, i.e. how strong the associationis between the drug pattern and the ADR. It should be noted that theassociation score corresponding to an association of a set of drugs(e.g., ABC, AB, AC, BC, and so on) with one or more ADRs (e.g., P or anyhigher order combination of ADRs containing P), can be generated usingany statistical tests or measures, such as the support metrics andconfidence metrics mentioned above and/or other metrics or scores, suchas Chi-square, mutual-information, or any other suitable statisticaltest or measure.

All these statistical tests or measures can be computed based on acontingency table, such as the contingency table shown in FIG. 2, forexample. In statistics, a contingency table is a type of table in amatrix format that displays the (multivariate) frequency distribution ofthe variables. Contingency tables provide a basic picture of theinterrelation between two variables and can help find interactionsbetween them.

As shown in FIG. 2, each entry in the contingency table 200 denotes thenumber of case reports, e.g., patients or patient EMRs, from the RWE 110data that satisfies the conditions of the rows and columns of thecontingency table 200, e.g., each case, or patient EMR, that has eitherthe complete drug pattern (row 210) or none of the drugs in the completedrug pattern (row 220), and which is either associated with the ADR(column 230) or not associated with the ADR (column 240) in the case orpatient EMR. A contingency table 200 may be generated for each drugpattern, e.g., ABC, and sub-drug pattern, e.g., AB, AC, and BC, and acorresponding evaluation based on the contingency table 200 for eachdrug pattern and sub-drug pattern may be performed by the co-occurrencelogic module 120.

In the example depicted in FIG. 2, only ‘AND’ logic is considered andnot ‘OR’ logic throughout. Thus, a pattern, or rule, containing drugs“ABC” means A ‘AND’ B ‘AND’ C. Therefore, if a case, e.g. patient EMR,includes only some of the drugs, then the case does not contain therule. For example, a patient case containing drugs ABDE is satisfyingthe rule (AB→P), but not (ABC→P).

By evaluating the various associations, it can be determined that notall co-occurrences of drugs A, B, and C with the ADR of P represent realinteractions among the drugs A, B and C. Rather, they can representconfounders due to co-prescribed drugs due to comorbidities of patients.Thus, there is a need to find the difference of the higher ordercombination ABC and each of its sub-drug combinations, AB, AC, and BC.If the lower-order drug combinations and the higher order drugcombinations have similar association score, e.g., an association scorebased on the defined metrics, such as support and confidence metrics,associated with an ADR, then confounder drugs may be identified andremoved so as to identify the real multi-drug correlations with ADRs.For example, if AC has a relatively low association score but ABC has arelatively higher association score, then the drug C may be considered aconfounder. On the other hand, if these sub-drug combinations haveassociation scores that are relatively low, and the association scoresof the higher-level drug combinations are relatively higher, then realhigher-order interactions can be identified.

In particular, the confounder filter logic module 130 may implementlogic that generates a newly designed metric, referred to as“Improvement”, which may be evaluated by the confounder filter logicmodule 130 for a particular combination of drugs to measure theirassociation with ADRs that is beyond the association of any of itssubsets of drugs with the same ADRs. In the case of an example of drugpattern ABC and its association with an adverse drug reaction P, i.e.ABC→P, the Improvement score may be denoted as follows:

${{Imp}\left( {ABC}\rightarrow P \right)} = {{{Score}\left( {ABC}\rightarrow P \right)} - {\max\limits_{({\alpha \in {ABC}})}{{Score}\left( \alpha\rightarrow P \right)}}}$

where Imp is the Improvement metric for the association of the drugpattern with an ADR, i.e. ABC with P in this example, and Score is theassociation score of the drug pattern with the ADR, which can be thesupport, confidence, Chi-square, mutual information, etc. This equationchecks how much the Score improved in this higher order pattern (ABC→P)from any of its sub-patterns, as denoted by α. From all suchsub-patterns a, the max Scores are considered of all such α's. ThisImprovement score will be very low if any of drugs in the drug patternis a simple confounder (e.g., if C is always co-prescribed with the drugA and B, then the score (ABC→P) will be the same as the score (AB→P)).On the other hand, the Improvement score will be higher for the trueinteractions among drugs and ADRs, and thus it will be able to filterout true interactions among multiple drugs with ADRs from the simpleconfounders.

The confounder filter logic module 130 calculates, for each of the drugpatterns, the Improvement score and compares the Improvement scores to athreshold Improvement score value. If the Improvement score is equal toor above the threshold Improvement score value, then it is determinedthat there are no confounders in the drug pattern. If the Improvementscore is below the threshold Improvement score value, then it isdetermined that the drug pattern contains a confounder and theconfounder is identified using the sub-drug patterns associated with thedrug pattern. For example, if it is found that Imp(ABC→P) is low andImp(AB→P) is high, then C is a confounder and the real drug-druginteraction that has a causal relationship with the ADR of P is AB→P.

Having filtered out any confounder drugs in the drug patterns based onthe evaluation of the Improvement scores, the remaining drug patternsare operated on by the causal association logic module 140 to infer acausal model 145 on all of the remaining drug patterns for a certain ADRgroup to enhance the interpretability of the model. The ADR group may bea single ADR or a plurality of ADRs categorized into a same ADR groupbased on similar characteristics. The ADR group may be defined a priori,such as based on medical resource information 125, subject matter expertconfiguration of the framework 100, or the like. For example, thesubject matter expert configuration of the framework 100 may includepredefined relationships among ADR terms. Such relationships among ADRscan be defined based on their common symptoms or other medicalontologies defined by different terms, such as ICD-9 codes.

For example, in accordance with one illustrative embodiment, all drugpatterns, including sub-drug patterns, remaining after the operation ofthe confounder filter logic module 130 to filter out drug patternshaving confounder drugs, are summarized in a Bayesian learning frameworkusing a directed acyclic graph. Basically, a Bayesian network isinferred for each ADR or ADR group of interest from the potential causalrules involving that ADR or ADR group based on the operation of theconfounder filter logic module 130. Note that, the singleton rules caneasily be represented in a directed acyclic graph (DAG), however thehigher order patterns consisting of multiple drugs require somemodifications so that they can also be represented by a DAG. Withoutloss of generalizability, the illustrative embodiments represent allsuch patterns containing multiple drugs into a new dummy variable. Forexample, a rule of ABC→P is represented by β₁→P, where β₁ represents theinteraction among the three drugs A, B and C. Here, the new dummyvariable is considered as a binary variable along with other singletonpatterns which contain the same ADR or ADR groups. Once, all suchpatterns are represented in a DAG, the transition probabilities amongthe variables are learned, which provides the final causal model 145.

Based on the causal model 145, multiple drug/ADR relationship inferencelogic 150 may be implemented to infer the causal relationships amongmultiple drugs that are responsible for a particular ADR or ADR group.In particular, the rules may be extracted from the Bayesian network foreach ADR under consideration as the final set of rules containing drugsand ADRs. Also, the confounders obtained from the confounder filterlogic module 130 can also be output as common confounders to the commonconfounders logic module 160 which may output such common confounderinformation and/or perform other processing of the common confounders,such as updating or generating additional resource information toimprove resource information sources 125 to specifically identify thecommon confounders, or the like. The inferred causal relationships andconfounders may be used to improve the interpretability among therelationships among the drugs and ADRs. This knowledge can further beused during clinical decision making by physicians and other medicalpersonnel treating patients by identifying real multiple druginteraction relationships with ADRs and identifying confounder drugs tothe clinical decision makers.

It should be noted that the inferred causal relationships may beutilized by a cognitive system, such as a cognitive decision supportsystem for assisting medical practitioners in treating patients, as aninformation source for assisting with the cognitive operations performedby the cognitive system. For example, if a treatment is being evaluatedfor a patient, and the treatment comprises one or more drugs that have acausal relationship with an ADR as determined through the mechanisms ofthe illustrative embodiments, an evaluation may further be made as towhether the patient is also prescribed or taking another drug in a drugpattern that has a causal relationship with the ADR. If so, thecognitive system may modify rankings or scores associated with suchtreatments to reflect the lower preference for selecting that treatmentand may provide information in association with an output of thetreatment recommendation indicating the causal relationship between thedrug, in combination with one or more other drugs, and the ADR.

In some cases, the causal relationship information may be used as abasis for outputting a notification to medical personnel, drugproviders, governmental organizations, or the like, to inform them ofthe identified causal relationships between the drug patterns andcorresponding ADRs. For example, such notifications may be used as a wayto update drug label information, medical reference documents,government guideline documents, or the like. The notification mayspecify the drug patterns and the associated ADRs, as well as a basisfor indicating the association between the drug patterns and the ADRs,e.g., the association scores, the Improvement scores, and any confounderdrugs identified through the operation of the present invention.

Moreover, the identification of confounders by the confounder filterlogic 130 may be used by common confounder identification logic 160 toidentify common confounders for particular ADRs and/or ADR groups. Thisinformation may be used for notification purposes and/or may be used bya cognitive system to assist with identifying confounder drugsprescribed to patients, as discussed hereafter.

In some illustrative embodiments, a patient model 175 can be generatedby patient model generation logic 170, based on the results of thedeterminations that identify the causal relationships between drugpatterns and an ADR, as well as the removal of confounders. This patientmodel 175 may then be used to predict ADRs for other patients based onthe drug history information for these other patients, which can beleveraged for clinical decision making. In particular, the Bayesianmodel generated by the causal association logic module 140 as the causalmodel 145 can be used to find the ADRs with highest probability for thepatient's drug history by comparing the rules in the causal model 145 tothe drug history of the patient's specific EMR data, i.e. finding thedrugs specified in the drug history of the patient EMR data that matchdrugs specified in the rules of the causal model 145 with which an ADRis associated and providing a corresponding notification to indicate aprobability of an ADR being associated with the patient. This patientmodel 175 may be provided to, or integrated into, a cognitive systemwhich may assist with such clinical decision making by predicting, for aparticular patient, based on their drug history information in theirpatient EMR data, whether the patient is likely to experience the ADRsassociated with the drugs that the patient is taking.

From the above description, it should be apparent that the illustrativeembodiments may be utilized in many different types of data processingenvironments. In order to provide a context for the description of thespecific elements and functionality of the illustrative embodiments,FIGS. 3-6 are provided hereafter as example environments in whichaspects of the illustrative embodiments may be implemented. It should beappreciated that FIGS. 3-6 are only examples and are not intended toassert or imply any limitation with regard to the environments in whichaspects or embodiments of the present invention may be implemented. Manymodifications to the depicted environments may be made without departingfrom the spirit and scope of the present invention.

FIGS. 3-6 are directed to describing an example cognitive system forhealthcare applications (also referred to herein as a “healthcarecognitive system”) which implements a request processing pipeline, suchas a Question Answering (QA) pipeline (also referred to as aQuestion/Answer pipeline or Question and Answer pipeline) for example,request processing methodology, and request processing computer programproduct with which the mechanisms of the illustrative embodiments areimplemented. These requests may be provided as structure or unstructuredrequest messages, natural language questions, or any other suitableformat for requesting an operation to be performed by the healthcarecognitive system. As described in more detail hereafter, the particularhealthcare application that is implemented in the cognitive system ofthe present invention is a healthcare application for generatingtreatment recommendations for patients or cognitive evaluation of apatient's EMR data for various decision support operations, based on acognitive evaluation of their medical condition(s), patient attributes,available candidate treatments as indicated in one or more corpora ofmedical documentation, and the like. In particular, with specificimportance to the present application, drug-drug interaction informationand association with adverse drug reactions (ADRs) is learned andapplied during evaluation of candidate treatments for the patient so asto select and present treatment recommendations that take intoconsideration the drug-drug interactions of the various treatments aswell as with other drugs the patient may be taking for other medicalconditions.

In some embodiments, the healthcare cognitive system may be employed tosimply review a patient's EMR data to identify potential or predictedADRs based on the drug history information in the patient's EMR data andthe learned associations of drug patterns with ADRs or ADR groups asdescribed previously. Notifications may output to medical practitionerscomprising the identification of the ADR predictions, the reasons forsuch ADR predictions, confounder drugs identified, and/or otherinformation about the applicable drug patterns and associated ADRs so asto assist them with treating the patient for their medical conditions.In still other illustrative embodiments, such notifications ofassociations of drug patterns with ADRs or ADR groups may beautomatically generated and output to appropriate drug providers,industry or governmental oversight organizations, or the like, so as toinform them of the identified drug pattern associations with ADRs andthereby provide them with information that may be used to update druglabels, medical reference documentation, governmental or industryguidelines, and the like.

It should be appreciated that the healthcare cognitive system, whileshown as having a single request processing pipeline in the exampleshereafter, may in fact have multiple request processing pipelines. Eachrequest processing pipeline may be separately trained and/or configuredto process requests associated with different domains or be configuredto perform the same or different analysis on input requests (orquestions in implementations using a QA pipeline), depending on thedesired implementation. For example, in some cases, a first requestprocessing pipeline may be trained to operate on input requests directedto a first medical malady domain (e.g., various types of blood diseases)while another request processing pipeline may be trained to answer inputrequests in another medical malady domain (e.g., various types ofcancers). In other cases, for example, the request processing pipelinesmay be configured to provide different types of cognitive functions orsupport different types of healthcare applications, such as one requestprocessing pipeline being used for patient diagnosis, another requestprocessing pipeline being configured for medical treatmentrecommendation, another request processing pipeline being configured forpatient monitoring, etc.

Moreover, each request processing pipeline may have their own associatedcorpus or corpora that they ingest and operate on, e.g., one corpus forblood disease domain documents and another corpus for cancer diagnosticsdomain related documents in the above examples. In some cases, therequest processing pipelines may each operate on the same domain ofinput questions but may have different configurations, e.g., differentannotators or differently trained annotators, such that differentanalysis and potential answers are generated. The healthcare cognitivesystem may provide additional logic for routing input questions to theappropriate request processing pipeline, such as based on a determineddomain of the input request, combining and evaluating final resultsgenerated by the processing performed by multiple request processingpipelines, and other control and interaction logic that facilitates theutilization of multiple request processing pipelines.

As noted above, one type of request processing pipeline with which themechanisms of the illustrative embodiments may be utilized is a QuestionAnswering (QA) pipeline. The description of example embodiments of thepresent invention hereafter will utilize a QA pipeline as an example ofa request processing pipeline that may be augmented to includemechanisms in accordance with one or more illustrative embodiments. Itshould be appreciated that while the present invention will be describedin the context of the cognitive system implementing one or more QApipelines that operate on an input question, the illustrativeembodiments are not limited to such. Rather, the mechanisms of theillustrative embodiments may operate on requests that are not posed as“questions” but are formatted as requests for the cognitive system toperform cognitive operations on a specified set of input data using theassociated corpus or corpora and the specific configuration informationused to configure the cognitive system. For example, rather than askinga natural language question of “What treatment applies to patient P?”,the cognitive system may instead receive a request of “generate atreatment for patient P,” or the like. As another example, a questionmay be of the type “What adverse drug reactions is the patient likely tohave?”, while a request may be of the type “Identify adverse drugreactions for this patient.” It should be appreciated that themechanisms of the QA system pipeline may operate on requests in asimilar manner to that of input natural language questions with minormodifications. In fact, in some cases, a request may be converted to anatural language question for processing by the QA system pipelines ifdesired for the particular implementation.

As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, augment, and extend the functionalityof these QA pipeline, or request processing pipeline, mechanisms of ahealthcare cognitive system with regard to providing treatmentrecommendations for patients that take into consideration drug-to-druginteractions when evaluating the efficacy of the treatment for aparticular patient and the particular patient's attributes, medicalcondition(s), other drugs being taken for treatment of the same or othermedical conditions, and the like. In taking into account the drug-druginteractions, the mechanisms of the illustrative embodiments may learnand identify drug patterns that are associated with adverse drugreactions (ADRs), and then may apply the learned associations, which maybe defined in terms of a causal model generated by the mechanisms of theillustrative embodiments, or a patient model as discussed above, to theprediction of potential ADRs for a patient, the evaluation of acandidate treatment based on the learned multiple drug pattern and ADRassociations with the patient's own personal medical conditions,attributes, and treatments, such as may be determined from patientelectronic medical records (EMRs), or the like. The prediction orevaluation of ADRs based on the drug history of a patient may be used tomodify the ranking or confidence values associated with candidatetreatments, indicating a confidence that the candidate treatment is aviable treatment for the particular patient, e.g., if there is alikelihood of an ADR, then the confidence may be reduced. The resultingranked listing of candidate treatments may then be used to select one ormore treatment recommendations to be output to a medical practitioner toassist the medical practitioner is treating the patient. Moreover,notifications of the predicted ADRs may be output to the medicalpractitioners and/or appropriate oversight organizations.

As some of the illustrative embodiments may be implemented in, or inconjunction with, a healthcare cognitive system, it is beneficial tounderstand how such cognitive systems, and question and answer creationin a cognitive system implementing a QA pipeline, is implemented. Itshould be appreciated that the mechanisms described in FIGS. 3-6 areonly examples and are not intended to state or imply any limitation withregard to the type of cognitive system mechanisms with which theillustrative embodiments are implemented. Many modifications to theexample cognitive system shown in FIGS. 3-6 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

Moreover, some illustrative embodiments of the present invention may beimplemented independent of any cognitive system. That is, the mechanismsdescribed previously with regard to FIGS. 1 and 2 may be implemented asa separate entity which operates to learn associations of drug patternswith ADRs for purposes of notification and generation of models that mayassist medical practitioners and/or providers or drugs to avoid suchADRs in patients. In other words, while some illustrative embodimentsmay integrate the framework 100 of the illustrative embodiments into theoperation of a cognitive system, other embodiments may not include thecognitive system or require the cognitive system. Hence, again, FIGS.3-6 are only examples of possible implementations of illustrativeembodiments and are not intended to be limiting on the possibleimplementations or embodiments.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)based logic, for example, and machine learning logic, which may beprovided as specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware.The logic of the cognitive system implements the cognitive operation(s),examples of which include, but are not limited to, question answering,identification of related concepts within different portions of contentin a corpus, intelligent search algorithms, such as Internet web pagesearches, for example, medical diagnostic and treatment recommendations,and other types of recommendation generation, e.g., items of interest toa particular user, potential new contact recommendations, or the like.

IBM Watson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like high accuracy at speeds far faster than human beings andon a larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypothesis    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situational awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system) and/or process requests which may or maynot be posed as natural language questions. The QA pipeline or system isan artificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area, e.g., financial domain, medical domain, legaldomain, etc., where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to cognitive system which implements theQA pipeline. The QA pipeline then answers the input questions using thecontent in the corpus of data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using Natural LanguageProcessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus, generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information usable by the QA pipeline to identify thesequestion and answer attributes of the content. The annotated content maybe processed to generate an in-memory representation of the documents inthe corpus/corpora. This process is sometimes referred to as ingestionof the documents or the corpus/corpora, which may result in an indexedset of documents with metadata specifying features of the documents.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 3 depicts a schematic diagram of one illustrative embodiment of acognitive system 300 implementing a request processing pipeline 308,which in some embodiments may be a question answering (QA) pipeline, ina computer network 302. For purposes of the present description, it willbe assumed that the request processing pipeline 308 is implemented as aQA pipeline that operates on structured and/or unstructured requests inthe form of input questions.

The cognitive system 300 is implemented on server computing device 305,which may comprise one or more computing devices comprising one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like. The server computingdevice 305 is connected to the computer network 302. For purposes ofillustration only, FIG. 3 depicts the cognitive system 100 beingimplemented on computing device 305 only, but in some illustrativeembodiments, the cognitive system 300 may be distributed across multiplecomputing devices, such as one or more of a plurality of computingdevices 304 and 305.

The network 302 includes multiple computing devices 304 and 305, whichmay operate as server computing devices, and 310-312 which may operateas client computing devices, in communication with each other and withother devices or components via one or more wired and/or wireless datacommunication links, where each communication link comprises one or moreof wires, routers, switches, transmitters, receivers, or the like. Insome illustrative embodiments, the cognitive system 300 and network 302enables question processing and answer generation (QA) functionality forone or more cognitive system users via their respective computingdevices 310-312. In other embodiments, the cognitive system 300 andnetwork 302 may provide other types of cognitive operations including,but not limited to, request processing and cognitive response generationwhich may take many different forms depending upon the desiredimplementation, e.g., cognitive information retrieval,training/instruction of users, cognitive evaluation of data, or thelike. Other embodiments of the cognitive system 300 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The cognitive system 300 is configured to implement a request processingpipeline 308 that receive inputs from various sources. The requests maybe posed in the form of a natural language question, natural languagerequest for information, natural language request for the performance ofa cognitive operation, or the like. For example, the cognitive system300 receives input from the network 302, a corpus or corpora ofelectronic documents 306, 340, cognitive system users, and/or other dataand other possible sources of input. In one embodiment, some or all ofthe inputs to the cognitive system 300 are routed through the network302. The various computing devices 304, 305, 310, 312 on the network 302include access points for content creators and cognitive system users.Some of the computing devices 304, 305 include devices for a databasestoring the corpus or corpora of data 306, 340. Portions of the corpusor corpora of data 306, 340 may also be provided on one or more othernetwork attached storage devices, in one or more databases, or othercomputing devices not explicitly shown in FIG. 3. The network 302includes local network connections and remote connections in variousembodiments, such that the cognitive system 300 may operate inenvironments of any size, including local and global, e.g., theInternet.

In one embodiment, the content creator creates content in a document ofthe corpus or corpora of data 306, 340 for use as part of a corpus ofdata with the cognitive system 300. The document includes any file,text, article, or source of data for use in the cognitive system 300.Cognitive system users access the cognitive system 300 via a networkconnection or an Internet connection to the network 302, and inputquestions/requests to the cognitive system 300 that areanswered/processed based on the content in the corpus or corpora of data106, 340. In one embodiment, the questions/requests are formed usingnatural language. The cognitive system 300 parses and interprets thequestion/request via a pipeline 308, and provides a response to thecognitive system user, e.g., cognitive system user 310, containing oneor more answers to the question posed, response to the request, resultsof processing the request, or the like. In some embodiments, thecognitive system 300 provides a response to users in a ranked list ofcandidate answers/responses while in other illustrative embodiments, thecognitive system 300 provides a single final answer/response or acombination of a final answer/response and ranked listing of othercandidate answers/responses.

The cognitive system 300 implements the pipeline 308 which comprises aplurality of stages for processing an input question/request based oninformation obtained from the corpus or corpora of data 306, 340. Thepipeline 308 generates answers/responses for the input question orrequest based on the processing of the input question/request and thecorpus or corpora of data 306. The pipeline 308 will be described ingreater detail hereafter with regard to FIG. 6.

In some illustrative embodiments, the cognitive system 300 may be theIBM Watson cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a pipeline of the IBM Watson cognitive systemreceives an input question or request which it then parses to extractthe major features of the question/request, which in turn are then usedto formulate queries that are applied to the corpus or corpora of data306, 340. Based on the application of the queries to the corpus orcorpora of data 306, 340, a set of hypotheses, or candidateanswers/responses to the input question/request, are generated bylooking across the corpus or corpora of data 306, 340 for portions ofthe corpus or corpora of data 306, 340 that have some potential forcontaining a valuable response to the input question/response (hereafterassumed to be an input question). The pipeline 308 of the IBM Watson™cognitive system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus 306, 340 found during the application of the queries using avariety of reasoning algorithms.

The scores obtained from the various reasoning algorithms are thenweighted against a statistical model that summarizes a level ofconfidence that the pipeline 108 of the IBM Watson™ cognitive system300, in this example, has regarding the evidence that the potentialcandidate answer is inferred by the question. This process is repeatedfor each of the candidate answers to generate a ranked listing ofcandidate answers which may then be presented to the user that submittedthe input question, e.g., a user of client computing device 310, or fromwhich a final answer is selected and presented to the user. Moreinformation about the pipeline 308 of the IBM Watson™ cognitive system300 may be obtained, for example, from the IBM Corporation website, IBMRedbooks, and the like. For example, information about the pipeline ofthe IBM Watson™ cognitive system can be found in Yuan et al., “Watsonand Healthcare,” IBM developerWorks, 2011 and “The Era of CognitiveSystems: An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

As noted above, while the input to the cognitive system 300 from aclient device may be posed in the form of a natural language question,the illustrative embodiments are not limited to such. Rather, the inputquestion may in fact be formatted or structured as any suitable type ofrequest which may be parsed and analyzed using structured and/orunstructured input analysis, including but not limited to the naturallanguage parsing and analysis mechanisms of a cognitive system such asIBM Watson™, to determine the basis upon which to perform cognitiveanalysis and providing a result of the cognitive analysis. In the caseof a healthcare based cognitive system, this analysis may involveprocessing patient medical records, medical guidance documentation fromone or more corpora, drug-drug interaction information and adverse drugreaction information in patient electronic medical records (EMRs) and/orin natural language documents, and the like, to provide a healthcareoriented cognitive system result.

In the context of the present invention, cognitive system 300 mayprovide a cognitive functionality for assisting with healthcare basedoperations. For example, depending upon the particular implementation,the healthcare based operations may comprise patient diagnostics,medical treatment recommendation systems, medical practice managementsystems, personal patient care plan generation and monitoring, patientelectronic medical record (EMR) evaluation for various purposes, such asfor identifying patients that are suitable for a medical trial or aparticular type of medical treatment, or the like. Thus, the cognitivesystem 300 may be a healthcare cognitive system 300 that operates in themedical or healthcare type domains and which may process requests forsuch healthcare operations via the request processing pipeline 308 inputas either structured or unstructured requests, natural language inputquestions, or the like. In some illustrative embodiments, the cognitivesystem 300 is a cognitive healthcare system that provides medicaltreatment recommendations for patients and their medical condition(s)based on a variety of factors which includes, among other factors,drug-to-drug interactions as learned and applied by the mechanisms ofone or more of the illustrative embodiments described herein.

As shown in FIG. 3, the cognitive system 300 is further augmented, inaccordance with the mechanisms of the illustrative embodiments, toinclude logic implemented in specialized hardware, software executed onhardware, or any combination of specialized hardware and softwareexecuted on hardware, for implementing a multiple drug pattern/adversedrug reaction (ADR) association learning framework 100, which in thisdepicted embodiment includes the co-occurrence logic module 120, theconfounder filter logic module 130, causal association logic module 140,causal model 145, multiple drug/ADR relationship inference logic 150,common confounder logic 160, patient model generation logic 170, andpatient model 175. The RWE data 110 and resource information sources 125shown in FIG. 1 may be part of one or more of the corpora 306, 340 shownin FIG. 3 and may be accessed by the framework 100 via one or morenetwork based connections and/or local connections as needed. It shouldbe appreciated that the framework 100 and the components 120-175 of theframework 100 operate in the manner previously described above withregard to FIGS. 1 and 2.

With regard to the interaction of the framework 100 with the cognitivesystem 300, this interaction may take many different forms dependingupon the desired implementation and particular illustrative embodiment.As noted above, the framework 100 provides identification ofassociations of multiple drug patterns with ADRs by inferring suchrelationships from the causal model built by identifying co-occurrencesof drug patterns and ADRs, such as by module 120, filtering out ofconfounder drugs, such as by module 130, and generating a causal model145, such as by way of module 140. This causal model 145 is evaluated toinfer relationships between multiple drug patterns and ADRs or ADRgroups, such as by logic 150. Moreover, common confounder drugs for ADRsor ADR groups are identified, such as by logic 160. Furthermore, apatient model 175 may be generated, such as by patient model generationlogic 170, which may be used by a cognitive system 300 to predict thelikelihood of a patient experiencing an ADR, based on the learned andinferred multiple drug pattern/ADR relationships.

The multiple drug pattern/ADR relationships, common confounder drugs forADRs or ADR groups, and the patient model may be provided as informationsources to the cognitive system 300 which is modified to operate on suchinformation when performing cognitive operations, as previouslydiscussed above. For example, such information may be evaluated by therequest processing pipeline 308 of the cognitive system 300 whenevaluating candidate treatments for a patient, evaluating a patient'sEMR data in general, evaluating a patient for candidacy for a medicaltrial, generating notifications to medical practitioners regardingidentified drug/ADR relationships, sending notifications to drugproviders and/or oversight organizations regarding identified drug/ADRrelationships, and the like.

The framework 100 learns, through a machine learning process, thedrug-drug interactions, or multiple drug patterns, that are associatedwith particular ADRs or ADR groups and which drugs are confounders insuch patterns. In some illustrative embodiments, the framework 100 mayfurther comprise logic that may be used to further evaluate the specificinformation for a patient, such as the drugs being taken by a patientfor the medical condition(s) of the patient, candidate treatments andtheir corresponding drugs which are being considered for treating apatient, as determined by the cognitive system, and the like. Theevaluation may provide an output indicating the ADRs that the patientmay be predicted to experience based on the drug information associatedwith the patient, the candidate treatments, etc. Moreover, the framework100 may provide indications of confounder drugs associated with thepatient information, the candidate treatments, etc. This information maybe provided back to the cognitive system for use in performing cognitiveoperations, such as for use in generating confidence scores associatedwith candidate treatments, ranking of candidate treatments, providingnotifications, and the like.

It should be appreciated that the various components of the framework100 show in the figures may be provided as software instructions storedin memory and executed by one or more processors of one or more dataprocessing systems that are specifically configured to implement theframework 100, which may be one or more of the processors provided bythe server computing device 105, or distributed over a plurality ofcomputing devices, such as server 105 and one or more of the servers104. Alternatively, some of the logic provided by the components of theframework 100 may be embodied in hardware devices, firmware, or thelike. Moreover, while the components of the framework 100 are shown inthe figures as example components to illustrate the operation of theillustrative embodiments, it should be appreciated that the framework100 may comprise additional logic that is not specifically shown in thefigures but which may support and assist with the functionality of thecomponents 120-175. Unless otherwise indicated herein, operations orfunctions described as being performed by the framework 100 that are notspecifically attributed to one of the components 120-175 may beperformed by this other logic provided in the framework 100 that is notspecifically shown, e.g., controller logic, interface logic, storagelogic, and/or the like.

Moreover, while the framework 100 is shown as a separate entity from thecognitive system 300 in FIG. 3 for ease of depiction, it should beappreciated that one or more of the components 120-175 may be integratedin and/or utilize logic and resources of the cognitive system 300 toperform their operations. For example, the framework 100 may make use ofthe natural language processing (NLP) and annotator mechanisms of thecognitive system 300 to perform operations for parsing and identifyingdrug-drug interaction, or drug pattern, and ADR association informationin natural language documents. Moreover, the patient model 175 may beintegrated with or operate in conjunction with scoring and ranking logicof the cognitive system 300 for scoring and ranking candidate answers,or candidate treatments, in order to select one or more candidatetreatments to output as treatment recommendations.

As noted above with regard to FIGS. 1 and 2, the illustrativeembodiments provide mechanisms for learning relationships orassociations of multiple drug patterns with ADRs or ADR groups andconfounder drugs in such multiple drug patterns. This machine learningof relationships of multiple drug patterns with ADRs and confounderdrugs may be used to build predictive models that can be used to predictwhether a particular patient is likely to experience an ADR. Moreover,in some implementations, these models may be used to predict whethercandidate treatments for a patient will likely result in an ADR andthereby modify the applicability of the candidate treatment to thepatient or at least generate a notification of the potential of the ADRfor consideration by medical practitioners when treating the patient. Instill other illustrative embodiments, such machine learning ofrelationships may be used to send notifications to medicalpractitioners, drug providers and/or manufacturers, andindustry/governmental oversight organizations, to inform them of learnedassociations or relationships between multiple drug patterns and ADRs orADR groups, as well as confounder drugs.

The illustrative embodiments may modify the relative scoring and rankingof candidate treatments for the particular patient based on thedetermined drug-drug interactions and ADRs associated with the candidatetreatments. Furthermore, the illustrative embodiments may outputtreatment recommendations with information indicating the reasons forthe relative rankings of candidate treatments based on drug-druginteractions and associations with ADRs.

As noted above, the mechanisms of the illustrative embodiments arerooted in the computer technology arts and are implemented using logicpresent in such computing or data processing systems. These computing ordata processing systems are specifically configured, either throughhardware, software, or a combination of hardware and software, toimplement the various operations described above. As such, FIG. 4 isprovided as an example of one type of data processing system in whichaspects of the present invention may be implemented. Many other types ofdata processing systems may be likewise configured to specificallyimplement the mechanisms of the illustrative embodiments.

FIG. 4 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 400 is an example of a computer, such as server 305 or client 310in FIG. 3, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 4 represents a servercomputing device, such as a server 305, which implements a cognitivesystem 300 and request processing pipeline 308 augmented to include theadditional mechanisms of the illustrative embodiments described herein.Alternatively, the server computing device 305 may be specificallyconfigured to implement only the framework 100, as previously discussedabove, which may operate on its own or in conjunction with a separatecognitive system, such as cognitive system 300.

In the depicted example, data processing system 400 employs a hubarchitecture including North Bridge and Memory Controller Hub (NB/MCH)402 and South Bridge and Input/Output (I/O) Controller Hub (SB/ICH) 404.Processing unit 406, main memory 408, and graphics processor 410 areconnected to NB/MCH 402. Graphics processor 410 is connected to NB/MCH402 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 412 connectsto SB/ICH 404. Audio adapter 416, keyboard and mouse adapter 420, modem422, read only memory (ROM) 424, hard disk drive (HDD) 426, CD-ROM drive430, universal serial bus (USB) ports and other communication ports 432,and PCI/PCIe devices 434 connect to SB/ICH 404 through bus 438 and bus440. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 424 may be, for example, a flashbasic input/output system (BIOS).

HDD 426 and CD-ROM drive 430 connect to SB/ICH 404 through bus 440. HDD426 and CD-ROM drive 430 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 436 is connected to SB/ICH 404.

An operating system runs on processing unit 406. The operating systemcoordinates and provides control of various components within the dataprocessing system 400 in FIG. 4. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 10®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 400.

As a server, data processing system 400 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINTJX® operating system. Dataprocessing system 400 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 406.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 426, and are loaded into main memory 408 for execution byprocessing unit 406. The processes for illustrative embodiments of thepresent invention are performed by processing unit 406 using computerusable program code, which is located in a memory such as, for example,main memory 408, ROM 424, or in one or more peripheral devices 426 and430, for example.

A bus system, such as bus 438 or bus 440 as shown in FIG. 4, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 422 or network adapter 412 of FIG. 4, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 408, ROM 424, or a cache such as found in NB/MCH 402 in FIG. 4.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 3 and 4 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 3and 4. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 400 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 400 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 400 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 5 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment. The example diagram of FIG. 5 depicts an implementation of ahealthcare cognitive system 500 that is configured to provide medicaltreatment recommendations for patients. However, it should beappreciated that this is only an example implementation and otherhealthcare operations may be implemented in other embodiments of thehealthcare cognitive system 500 without departing from the spirit andscope of the present invention.

Moreover, it should be appreciated that while FIG. 5 depicts the patient502 and user 506 as human figures, the interactions with and betweenthese entities may be performed using computing devices, medicalequipment, and/or the like, such that entities 502 and 506 may in factbe computing devices, e.g., client computing devices. For example, theinteractions 504, 514, 516, and 530 between the patient 502 and the user506 may be performed orally, e.g., a doctor interviewing a patient, andmay involve the use of one or more medical instruments, monitoringdevices, or the like, to collect information that may be input to thehealthcare cognitive system 500 as patient attributes 518. Interactionsbetween the user 506 and the healthcare cognitive system 500 will beelectronic via a user computing device (not shown), such as a clientcomputing device 310 or 312 in FIG. 3, communicating with the healthcarecognitive system 500 via one or more data communication links andpotentially one or more data networks.

As shown in FIG. 5, in accordance with one illustrative embodiment, apatient 502 presents symptoms 504 of a medical condition to a user 506,such as a medical practitioner, technician, or the like. The user 506may interact with the patient 502 via a question 514 and response 516exchange where the user gathers more information about the patient 502,the symptoms 504, and the medical condition of the patient 502. Itshould be appreciated that the questions/responses may in fact alsorepresent the user 506 gathering information from the patient 502 usingvarious medical equipment, e.g., blood pressure monitors, thermometers,wearable health and activity monitoring devices associated with thepatient such as a FitBit™, a wearable heart monitor, or any othermedical equipment that may monitor one or more medical characteristicsof the patient 502. In some cases, such medical equipment may be medicalequipment typically used in hospitals or medical centers to monitorvital signs and medical conditions of patients that are present inhospital beds for observation or medical treatment.

In response, the user 502 submits a request 508 to the healthcarecognitive system 500, such as via a user interface on a client computingdevice that is configured to allow users to submit requests to thehealthcare cognitive system 500 in a format that the healthcarecognitive system 500 can parse and process. The request 508 may include,or be accompanied with, information identifying patient attributes 518,the medical condition diagnosed by the user 506, or the like. Thesepatient attributes 518 may include, for example, an identifier of thepatient 502 from which patient EMRs 522 for the patient may beretrieved, demographic information about the patient, the symptoms 504,and other pertinent information obtained from the responses 516 to thequestions 514 or information obtained from medical equipment used tomonitor or gather data about the condition of the patient 502. Anyinformation about the patient 502 that may be relevant to a cognitiveevaluation of the patient by the healthcare cognitive system 500 may beincluded in the request 508 and/or patient attributes 518.

The healthcare cognitive system 500 provides a cognitive system that isspecifically configured to perform an implementation specific healthcareoriented cognitive operation. In the depicted example, this healthcareoriented cognitive operation is directed to providing a treatmentrecommendation 528 to the user 506 to assist the user 506 in treatingthe patient 502 based on their reported symptoms 504, medical condition,and other information gathered about the patient 502 via the question514 and response 516 process and/or medical equipment monitoring/datagathering. The healthcare cognitive system 500 operates on the request508 and patient attributes 518 utilizing information gathered from themedical corpus and other source data 526, treatment guidance data 524,and the patient EMRs 522 associated with the patient 502 to generate oneor more treatment recommendation 528. The treatment recommendations 528may be presented in a ranked ordering with associated supportingevidence, obtained from the patient attributes 518 and data sources522-526, indicating the reasoning as to why the treatment recommendation528 is being provided and why it is ranked in the manner that it isranked.

For example, based on the request 508 and the patient attributes 518,the healthcare cognitive system 500 may operate on the request, such asby using a QA pipeline type processing as described herein, to parse therequest 508 and patient attributes 518 to determine what is beingrequested and the criteria upon which the request is to be generated asidentified by the patient attributes 518, and may perform variousoperations for generating queries that are sent to the data sources522-526 to retrieve data, generate candidate treatment recommendations(or answers to the input question), and score these candidate treatmentrecommendations based on supporting evidence found in the data sources522-526. In the depicted example, the patient EMRs 522 is a patientinformation repository that collects patient data from a variety ofsources, e.g., hospitals, laboratories, physicians' offices, healthinsurance companies, pharmacies, etc. The patient EMRs 522 store variousinformation about individual patients, such as patient 502, in a manner(structured, unstructured, or a mix of structured and unstructuredformats) that the information may be retrieved and processed by thehealthcare cognitive system 500. This patient information may comprisevarious demographic information about patients, personal contactinformation about patients, employment information, health insuranceinformation, laboratory reports, physician reports from office visits,hospital charts, historical information regarding previous diagnoses,symptoms, treatments, prescription information, etc. Based on anidentifier of the patient 502, the patient's corresponding EMRs 522 fromthis patient repository may be retrieved by the healthcare cognitivesystem 500 and searched/processed to generate treatment recommendations528.

The treatment guidance data 524 provides a knowledge base of medicalknowledge that is used to identify potential treatments for a patientbased on the patient's attributes 518 and historical informationpresented in the patient's EMRs 522. This treatment guidance data 524may be obtained from official treatment guidelines and policies issuedby medical authorities, e.g., the American Medical Association, may beobtained from widely accepted physician medical and reference texts,e.g., the Physician's Desk Reference, insurance company guidelines, orthe like. The treatment guidance data 524 may be provided in anysuitable form that may be ingested by the healthcare cognitive system500 including both structured and unstructured formats.

In some cases, such treatment guidance data 524 may be provided in theform of rules that indicate the criteria required to be present, and/orrequired not to be present, for the corresponding treatment to beapplicable to a particular patient for treating a particular symptom ormedical malady/condition. For example, the treatment guidance data 524may comprise a treatment recommendation rule that indicates that for atreatment of Decitabine, strict criteria for the use of such a treatmentis that the patient 502 is less than or equal to 60 years of age, hasacute myeloid leukemia (AML), and no evidence of cardiac disease. Thus,for a patient 502 that is 59 years of age, has AML, and does not haveany evidence in their patient attributes 518 or patient EMRs indicatingevidence of cardiac disease, the following conditions of the treatmentrule exist:

Age<=60 years=59 (MET);

Patient has AML=AML (MET); and

Cardiac Disease=false (MET)

Since all of the criteria of the treatment rule are met by the specificinformation about this patient 502, then the treatment of Decitabine isa candidate treatment for consideration for this patient 502. However,if the patient had been 69 years old, the first criterion would not havebeen met and the Decitabine treatment would not be a candidate treatmentfor consideration for this patient 502. Various potential treatmentrecommendations may be evaluated by the healthcare cognitive system 500based on ingested treatment guidance data 524 to identify subsets ofcandidate treatments for further consideration by the healthcarecognitive system 500 by scoring such candidate treatments based onevidential data obtained from the patient EMRs 522 and medical corpusand other source data 526.

For example, data mining processes may be employed to mine the data insources 522 and 526 to identify evidential data supporting and/orrefuting the applicability of the candidate treatments to the particularpatient 502 as characterized by the patient's patient attributes 518 andEMRs 522. For example, for each of the criteria of the treatment rule,the results of the data mining provide a set of evidence that supportsgiving the treatment in the cases where the criterion is “MET” and incases where the criterion is “NOT MET.” The healthcare cognitive system500 processes the evidence in accordance with various cognitive logicalgorithms to generate a confidence score for each candidate treatmentrecommendation indicating a confidence that the corresponding candidatetreatment recommendation is valid for the patient 502. The candidatetreatment recommendations may then be ranked according to theirconfidence scores and presented to the user 506 as a ranked listing oftreatment recommendations 528. In some cases, only a highest ranked, orfinal answer, is returned as the treatment recommendation 528. Thetreatment recommendation 528 may be presented to the user 506 in amanner that the underlying evidence evaluated by the healthcarecognitive system 500 may be accessible, such as via a drilldowninterface, so that the user 506 may identify the reasons why thetreatment recommendation 528 is being provided by the healthcarecognitive system 500.

One example of a healthcare cognitive system 500 which may beimplemented and modified to incorporate the operations of the cognitivetreatment recommendation system 540 of one or more of the illustrativeembodiments, is described in co-pending and commonly assigned U.S.patent application Ser. No. 15/262,311, filed Sep. 12, 2016 and entitled“Medical Condition Independent Engine for Medical TreatmentRecommendation System,” which is hereby incorporated by reference. Itshould be appreciated that this is only one example of a cognitivehealthcare system with which the mechanisms of the illustrativeembodiments may be utilized. The mechanisms of the illustrativeembodiments may be implemented with any cognitive healthcare system thatevaluates patient EMR data and candidate treatment data to generate atreatment recommendation for treating the patient.

In accordance with the illustrative embodiments herein, the healthcarecognitive system 500 is augmented to include, or operate in conjunctionwith, the multiple drug pattern/ADR association learning framework 100which operates in the manner described previously, with regard to thesimilar system 100 in FIG. 1 and one or more of the illustrativeembodiments described above. The depiction in FIG. 5 is showing runtimeoperation of the cognitive treatment recommendation system 500 forassisting with the evaluation of candidate treatments for a particularpatient. As such, it is assumed that the framework has already performedits initial operations for ingesting a corpus/corpus of information togenerate causal model data structures, confounder information datastructures, and the like, such as previously described above.

During runtime operation, the healthcare cognitive system 500 generatescandidate treatments for the patient and provides this information alongwith patient information, e.g., patient EMR information including drughistory information, to the framework 100. The framework 100, in thedepicted example embodiment, may comprises logic that analyzes thecandidate treatments to identify the drugs involved in the candidatetreatments, identifies the drugs indicated as being activelyadministered to the patient for the same or different medical conditionfor which the candidate treatments are being considered, to therebyidentify the drugs being taken by the patient and the drugs that arepotentially going to be administered to the patient should the variouscandidate treatments be selected for treating the patient. The multipledrug patterns and associated ADRs or ADR groups associated with theidentified drugs may be retrieved or accessed via the causal model 145,and indications of potential ADRs and confounder drugs may be generatedfor the candidate treatments. This information may be provided back tothe healthcare cognitive system 500 for use in evaluating the confidenceand/or ranking of the candidate treatments, such as by reducing theconfidence scores and/or ranking of candidate treatments that aredetermined to have a probability of generating and adverse drug reaction(ADR).

As mentioned previously, in some illustrative embodiments, thistreatment recommendation 528 may include a ranked listing of candidatetreatments with corresponding explanations as to why certain candidatetreatments are ranked lower based on drug-to-drug interactions. Theseexplanations may indicate the specific drugs in the drug-druginteractions, i.e. the multiple drug patterns, that are causing areduction in the ranking of the candidate treatment due to theprobability of an ADR. The output of the treatment recommendation 528may comprise a graphical user interface or other suitable notificationmechanism.

While FIG. 5 is depicted with an interaction between the patient 502 anda user 506, which may be a healthcare practitioner such as a physician,nurse, physician's assistant, lab technician, or any other healthcareworker, for example, the illustrative embodiments do not require such.Rather, the patient 502 may interact directly with the healthcarecognitive system 500 without having to go through an interaction withthe user 506 and the user 506 may interact with the healthcare cognitivesystem 500 without having to interact with the patient 502. For example,in the first case, the patient 502 may be requesting 508 treatmentrecommendations 528 from the healthcare cognitive system 500 directlybased on the symptoms 504 provided by the patient 502 to the healthcarecognitive system 500. Moreover, the healthcare cognitive system 500 mayactually have logic for automatically posing questions 514 to thepatient 502 and receiving responses 516 from the patient 502 to assistwith data collection for generating treatment recommendations 528. Inthe latter case, the user 506 may operate based on only informationpreviously gathered and present in the patient EMR 522 by sending arequest 508 along with patient attributes 518 and obtaining treatmentrecommendations in response from the healthcare cognitive system 500.Thus, the depiction in FIG. 5 is only an example and should not beinterpreted as requiring the particular interactions depicted when manymodifications may be made without departing from the spirit and scope ofthe present invention. It should be appreciated, however, that at notime should the treatment itself be administered to the patient 502without prior approval of the healthcare professional treating thepatient, i.e. final determinations as to treatments given to a patientwill always fall on the healthcare or medical professional with themechanisms of the illustrative embodiments serving only as an advisorytool for the healthcare or medical professional (user 506) and/orpatient 502.

As mentioned above, the healthcare cognitive system 500 may include arequest processing pipeline, such as request processing pipeline 308 inFIG. 3, which may be implemented, in some illustrative embodiments, as aQuestion Answering (QA) pipeline. The QA pipeline may receive an inputquestion, such as “what is the appropriate treatment for patient P?”, ora request, such as “diagnose and provide a treatment recommendation forpatient P.” In some cases, the QA pipeline may receive an input questionsuch as, “what ADRs is the patient likely to experience?” or a request,such as “tell me the ADRs the patient may experience.”

FIG. 6 illustrates a QA pipeline of a healthcare cognitive system, suchas healthcare cognitive system 500 in FIG. 5, or an implementation ofcognitive system 300 in FIG. 3, for processing an input question inaccordance with one illustrative embodiment. It should be appreciatedthat the stages of the QA pipeline shown in FIG. 6 are implemented asone or more software engines, components, or the like, which areconfigured with logic for implementing the functionality attributed tothe particular stage. Each stage is implemented using one or more ofsuch software engines, components or the like. The software engines,components, etc. are executed on one or more processors of one or moredata processing systems or devices and utilize or operate on data storedin one or more data storage devices, memories, or the like, on one ormore of the data processing systems. The QA pipeline of FIG. 6 isaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 600 may be provided for interfacingwith the pipeline 600 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 6, the QA pipeline 600 comprises a plurality of stages610-680 through which the cognitive system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 610, the QA pipeline 600 receives an input question that ispresented in a natural language format. That is, a user inputs, via auser interface, an input question for which the user wishes to obtain ananswer, e.g., “What medical treatments for diabetes are applicable to a60 year old patient with cardiac disease?” In response to receiving theinput question, the next stage of the QA pipeline 600, i.e. the questionand topic analysis stage 620, parses the input question using naturallanguage processing (NLP) techniques to extract major features from theinput question, and classify the major features according to types,e.g., names, dates, or any of a plethora of other defined topics. Forexample, in a question of the type “Who were Washington's closestadvisors?”, the term “who” may be associated with a topic for “persons”indicating that the identity of a person is being sought, “Washington”may be identified as a proper name of a person with which the questionis associated, “closest” may be identified as a word indicative ofproximity or relationship, and “advisors” may be indicative of a noun orother language topic. Similarly, in the previous question “medicaltreatments” may be associated with pharmaceuticals, medical procedures,holistic treatments, or the like, “diabetes” identifies a particularmedical condition, “60 years old” indicates an age of the patient, and“cardiac disease” indicates an existing medical condition of thepatient.

In addition, the extracted major features include key words and phrases,classified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referred to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?,” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “ drug”since if this word were replaced with the answer, e.g., the answer“Adderall” can be used to replace the term “drug” to generate thesentence “Adderall has been shown to relieve the symptoms of ADD withrelatively few side effects.” The focus often, but not always, containsthe LAT. On the other hand, in many cases it is not possible to infer ameaningful LAT from the focus.

Referring again to FIG. 6, the identified major features are then usedduring the question decomposition stage 630 to decompose the questioninto one or more queries that are applied to the corpora ofdata/information 645 in order to generate one or more hypotheses. Thequeries are generated in any known or later developed query language,such as the Structure Query Language (SQL), or the like. The queries areapplied to one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the corpora of data/information 645. That is, these various sourcesthemselves, different collections of sources, and the like, represent adifferent corpus 647 within the corpora 645. There may be differentcorpora 647 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 647 within the corpora 645.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries are applied to the corpus of data/informationat the hypothesis generation stage 640 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 640, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 640, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

The QA pipeline 600, in stage 650, then performs a deep analysis andcomparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this involvesusing a plurality of reasoning algorithms, each performing a separatetype of analysis of the language of the input question and/or content ofthe corpus that provides evidence in support of, or not in support of,the hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis. There are various ways of generating such scores dependingupon the particular analysis being performed. In generally, however,these algorithms look for particular terms, phrases, or patterns of textthat are indicative of terms, phrases, or patterns of interest anddetermine a degree of matching with higher degrees of matching beinggiven relatively higher scores than lower degrees of matching.

Thus, for example, an algorithm may be configured to look for the exactterm from an input question or synonyms to that term in the inputquestion, e.g., the exact term or synonyms for the term “movie,” andgenerate a score based on a frequency of use of these exact terms orsynonyms. In such a case, exact matches will be given the highestscores, while synonyms may be given lower scores based on a relativeranking of the synonyms as may be specified by a subject matter expert(person with knowledge of the particular domain and terminology used) orautomatically determined from frequency of use of the synonym in thecorpus corresponding to the domain. Thus, for example, an exact match ofthe term “movie” in content of the corpus (also referred to as evidence,or evidence passages) is given a highest score. A synonym of movie, suchas “motion picture” may be given a lower score but still higher than asynonym of the type “film” or “moving picture show.” Instances of theexact matches and synonyms for each evidence passage may be compiled andused in a quantitative function to generate a score for the degree ofmatching of the evidence passage to the input question.

Thus, for example, a hypothesis or candidate answer to the inputquestion of “What was the first movie?” is “The Horse in Motion.” If theevidence passage contains the statements “The first motion picture evermade was ‘The Horse in Motion’ in 1878 by Eadweard Muybridge. It was amovie of a horse running,” and the algorithm is looking for exactmatches or synonyms to the focus of the input question, i.e. “movie,”then an exact match of “movie” is found in the second sentence of theevidence passage and a highly scored synonym to “movie,” i.e. “motionpicture,” is found in the first sentence of the evidence passage. Thismay be combined with further analysis of the evidence passage toidentify that the text of the candidate answer is present in theevidence passage as well, i.e. “The Horse in Motion.” These factors maybe combined to give this evidence passage a relatively high score assupporting evidence for the candidate answer “The Horse in Motion” beinga correct answer.

It should be appreciated that this is just one simple example of howscoring can be performed. Many other algorithms of various complexitymay be used to generate scores for candidate answers and evidencewithout departing from the spirit and scope of the present invention.

In the synthesis stage 660, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QApipeline 600 and/or dynamically updated. For example, the weights forscores generated by algorithms that identify exactly matching terms andsynonym may be set relatively higher than other algorithms that areevaluating publication dates for evidence passages. The weightsthemselves may be specified by subject matter experts or learned throughmachine learning processes that evaluate the significance ofcharacteristics evidence passages and their relative importance tooverall candidate answer generation.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA pipeline 600 that identifies amanner by which these scores may be combined to generate a confidencescore or measure for the individual hypotheses or candidate answers.This confidence score or measure summarizes the level of confidence thatthe QA pipeline 600 has about the evidence that the candidate answer isinferred by the input question, i.e. that the candidate answer is thecorrect answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 670 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 680, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interfaceor other mechanism for outputting information.

As shown in FIG. 6, in accordance with one illustrative embodiment, theframework 100 may ingest and learns multiple drug interactions ormultiple drug patterns, and their associations with ADRs or ADR groups,as well as confounder drugs and the like, during an initial ingestionoperation for ingesting portions of the corpora 645, which may includepatient EMR data having drug history information, medical referencedocuments, etc. as previously discussed above. The framework 100 maygenerate a causal model 145 based on the processing of the components120-140 and a patient model 175 based on the causal model and theoperation of one or more of the components 150-170. These models may beused to identify such multiple drug pattern relationships with ADRs orADR groups.

As shown in FIG. 6, the various candidate treatments generated ashypotheses in the hypothesis generation stage 640 of the pipeline 600may be provided to the framework 100 which may comprise logic forapplying the learned multiple drug pattern/ADR relationships orassociations and confounder drug information to the candidate treatmentsand patient information to identify potential ADRs that the patient mayexperience. This may involve applying the patient model 175 and causalmodel 145 information to the specific combinations of drugs that thepatient is taking and may take based on the candidate treatments. Theframework 100 may then return any identified ADRs and confounderinformation for the candidate treatments back to the hypothesis andevidence scoring stage logic 650 of the pipeline which may utilize thatinformation in generating confidence scores and rankings for the variouscandidate treatments. The remainder of the processing by the pipeline600 may continue on as described above so as to generate one or morefinal candidate treatment answers.

Thus, the illustrative embodiments provide mechanisms for building acausal model that comprises rules that specify causal relationshipsbetween multiple drugs and corresponding adverse drug reactions (ADRs)or ADR groups. The building of this causal model comprises deep analysisof real world evidence (RWE), potentially based on various resource datastructures providing knowledge for evaluating terms, phrases, codes,etc., indicative of relationships between drugs and ADRs. The buildingof the causal model comprises identifying co-occurrences of drugs withADRs in the RWE, identifying confounder drugs in these co-occurrences,and filtering out the co-occurrences of drugs with ADRs that have suchconfounder drugs present. The causality model built may comprise therules that do not have confounder drugs present such that each ADR orADR group may have an associated set of one or more rules specifyingcausal relationships of two or more drugs with the ADR or ADR group.This causal model may then be applied to other patient data to determinea probability that the patient may encounter an ADR based on the drugsbeing taken by that patient. Such application of the causal model tospecific patient data may be performed in conjunction with othercognitive operations being performed by a cognitive system as notedabove. Thus, the operation of the cognitive system is improved by theimplementation of the causal model generated by the illustrativeembodiments which comprises actual real causal relationships withconfounders having been removed.

FIG. 7 is a flowchart outlining an example operation for learningmultiple drug pattern relationships with adverse drug reactions andfiltering out confounder drugs in accordance with one illustrativeembodiment. As shown in FIG. 7, the operation starts by receiving realworld evidence (RWE) for a plurality of patients (step 710).Co-occurrences of drugs with ADRs are identified in the RWE (step 720),which may involve applying knowledge obtained from various resource datastructures from various resource information sources and may involvevarious operations, such as various natural language processingoperations, as previously detailed above.

Confounder drugs in the identified co-occurrences are identified (step730). As noted above, this may involve a process of evaluatingimprovement scores and comparisons to thresholds to determine ifcombinations of drugs have a significant improvement oversub-combinations within these combinations, e.g., adding drug C to thesub-combination AB results in a significant enough improvement in theassociation of ABC with the ADR of P than the association score of AB toP. If it does not, then C is most likely a confounder drug.Co-occurrences that are determined to have confounder drugs present maybe eliminated from further consideration, e.g., filtered out (step 740).Although filtered out, the confounder drugs may be identified to areporting mechanism, such as common confounder logic 160 in FIG. 1, toreport such confounders to cognitive systems, to resource informationsources, or the like. A causality model is then built based on theremaining co-occurrences of drugs with ADRs and may be provided for useby a cognitive system (step 750). The operation may terminate at thispoint, however for purpose of completeness, the flowchart furtherillustrates the implementation of the causal model with the cognitivesystem to evaluate another patient.

Based on the causality model, for each ADR, a set of rules specifyingcausal relationships between combinations of drugs and the ADR aredetermined (step 760). The rules are applied to other patient drug datato generate a patient model for the other patient that specifies theprobability of ADRs based on drugs in the patient drug data (step 770).The patient model may be provided to a cognitive system (step 780) whichperforms cognitive operations, e.g., treatment recommendationoperations, based on the patient model, e.g., based on the probabilitiesof ADRs associated with the patient due to the drugs being taken by thepatient as indicated by the causal model built by the mechanisms of theillustrative embodiments (step 790). The operation then terminates.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11 a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

1. A method, in a data processing system comprising at least oneprocessor and at least one memory, the at least one memory comprisinginstructions executed by the at least one processor to cause the atleast one processor to implement a framework for learning multipledrug-adverse drug reaction associations and predicting patient adversedrug reactions (ADRs), the method comprising: analyzing, by aco-occurrence logic module of the framework, real world evidencecomprising patient electronic medical record (EMR) data and ADR data, toidentify co-occurrences of references to drugs with references toadverse drug reactions to thereby generate one or more drug-ADR rulesspecifying one or more drug-ADR relationships; generating, by a causalassociation logic module of the framework, a causal model based on theone or more drug-ADR rules, wherein the causal model comprises, for eachADR in a set of ADRs, a corresponding set of one or more rules, eachrule specifying one or more drugs having a causal relationship with theADR; and executing the causal model with a cognitive computing systemwhich processes other patient EMR data by applying the one or moredrug-ADR rules in the causal model to generate a prediction of a patientADR for a patient.
 2. The method of claim 1, wherein analyzing the realworld evidence comprises: identifying all co-occurrences of referencesto drugs with references to ADRs in the real world evidence; generatingthe one or more drug-ADR rules based on the identified co-occurrences,wherein each drug-ADR rule specifies a drug pattern identifying aplurality of drugs and a corresponding ADR; and selecting a sub-set ofthe one or more drug-ADR rules as a basis for generating the causalmodel based on at least one of a support metric or a confidence metric,wherein the support metric measures, for an associated drug-ADR rule, anumber of instances of the co-occurrence in the real world evidence, andwherein the confidence metric measures, for an associated drug-ADR rule,a probability of the associated ADR given the drugs in the drug patternof the associated drug-ADR rule.
 3. The method of claim 2, whereinidentifying all co-occurrences of references to drugs with reference toADRs in the real world evidence comprises: performing natural languageprocessing of the real world evidence to identify at least one of terms,phrases, or medical codes identifying references to drugs and referencesto ADRs; evaluating relative distances within the real world evidence,between each identified term, phrase or medical code identifyingreferences to drugs and references to ADRs; and identifyingco-occurrences based on the relative distances.
 4. The method of claim2, wherein selecting a sub-set of the one or more drug-ADR rulescomprises, for each drug-ADR rule: generating a contingency table datastructure, where each entry in the contingency table data structurecomprises a number of patient electronic medical records that satisfy acondition of the row and column of the contingency table data structurecorresponding to the entry, wherein: a first row of the contingencytable data structure corresponds to patient electronic medical recordsthat contain all drugs in the drug pattern of the drug-ADR rule, asecond row of the contingency table data structure corresponds topatient electronic medical records that contain none of the drugs in thedrug pattern of the drug-ADR rule, a first column of the contingencytable data structure corresponds to the patient electronic medicalrecords that contain the ADR in the drug-ADR rule, and a second columnof the contingency table data structure corresponds to the patientelectronic medical records that do not contain the ADR in the drug-ADRrule.
 5. The method of claim 1, further comprising: filtering, by aconfounder filter logic module of the framework, the one or moredrug-ADR rules to remove a subset of one or more drug-ADR rules havingconfounder drugs specified in the drug-ADR rules of the subset, andthereby generate a filtered set of drug-ADR rules, wherein the causalmodel is generated based on the filtered subset.
 6. The method of claim5, wherein each drug-ADR rule in the one or more drug-ADR rulesspecifies a corresponding drug pattern comprising a plurality of drugs,and corresponding adverse drug reaction, and wherein filtering the oneor more drug-ADR rules further comprises: calculating, for each firstdrug-ADR rule, an improvement metric specifying an amount of improvementof a corresponding association score for the first drug-ADR rule over anassociation score for another second drug-ADR rule specifying asub-pattern of the corresponding drug pattern of the first drug-ADRrule, and the corresponding adverse drug reaction of the first drug-ADRrule; and determining, for each first drug-ADR rule, whether to maintainthe first drug-ADR rule or remove the first drug-ADR rule based on avalue of the improvement metric.
 7. The method of claim 6, whereindetermining, for each first drug-ADR rule, whether to maintain the firstdrug-ADR rule or remove the first drug-ADR rule based on a value of theimprovement metric comprises; comparing the improvement metriccorresponding to the first drug-ADR rule to an improvement metricthreshold value; in response to the improvement metric corresponding tothe first drug-ADR rule not being equal to or greater than theimprovement metric threshold value, determining that a confounder drugis present in the corresponding drug pattern of the first drug-ADR rule;and identifying the confounder drug in the corresponding drug patternbased on a difference between the corresponding drug pattern and thesub-pattern.
 8. The method of claim 1, wherein executing the causalmodel with the cognitive computing system comprises: evaluating theother patient EMR data by applying the causal model to drug history datapresent in the other patient EMR data to identify probabilities of apatient encountering one or more ADRs in a set of ADRs; and generating,for the patient, a patient model based on the identified probabilitiesof the patient encountering one or more ADRs in the set of ADRs. 9.(canceled)
 10. The method of claim 8, wherein the cognitive computingsystem, based on the prediction of the patient ADR for the patient,automatically generates a treatment recommendation that is output to amedical practitioner based on an evaluation of the other patient EMRdata by the cognitive computing system, and the patient model.
 11. Acomputer program product comprising a computer readable storage mediumhaving a computer readable program stored therein, wherein the computerreadable program, when executed on a computing device, causes thecomputing device to: analyze, by a co-occurrence logic module executingon the computing device, real world evidence comprising patientelectronic medical record (EMR) data and ADR data, to identifyco-occurrences of references to drugs with references to adverse drugreactions to thereby generate one or more drug-ADR rules specifying oneor more drug-ADR relationships; generate, by a causal association logicmodule executing on the computing device, a causal model based on theone or more drug-ADR rules, wherein the causal model comprises, for eachADR in a set of ADRs, a corresponding set of one or more rules, eachrule specifying one or more drugs having a causal relationship with theADR; and execute the causal model with a cognitive computing systemwhich processes other patient EMR data by applying the one or moredrug-ADR rules in the causal model to generate a prediction of a patientADR for a patient.
 12. The computer program product of claim 11, whereinthe computer readable program further causes the computing device toanalyze the real world evidence at least by: identifying allco-occurrences of references to drugs with references to ADRs in thereal world evidence; generating the one or more drug-ADR rules based onthe identified co-occurrences, wherein each drug-ADR rule specifies adrug pattern identifying a plurality of drugs and a corresponding ADR;and selecting a sub-set of the one or more drug-ADR rules as a basis forgenerating the causal model based on at least one of a support metric ora confidence metric, wherein the support metric measures, for anassociated drug-ADR rule, a number of instances of the co-occurrence inthe real world evidence, and wherein the confidence metric measures, foran associated drug-ADR rule, a probability of the associated ADR giventhe drugs in the drug pattern of the associated drug-ADR rule.
 13. Thecomputer program product of claim 12, wherein the computer readableprogram further causes the computing device to identify allco-occurrences of references to drugs with reference to ADRs in the realworld evidence at least by: performing natural language processing ofthe real world evidence to identify at least one of terms, phrases, ormedical codes identifying references to drugs and references to ADRs;evaluating relative distances within the real world evidence, betweeneach identified term, phrase or medical code identifying references todrugs and references to ADRs; and identifying co-occurrences based onthe relative distances.
 14. The computer program product of claim 12,wherein the computer readable program further causes the computingdevice to select a sub-set of the one or more drug-ADR rules at leastby, for each drug-ADR rule: generating a contingency table datastructure, where each entry in the contingency table data structurecomprises a number of patient electronic medical records that satisfy acondition of the row and column of the contingency table data structurecorresponding to the entry, wherein: a first row of the contingencytable data structure corresponds to patient electronic medical recordsthat contain all drugs in the drug pattern of the drug-ADR rule, asecond row of the contingency table data structure corresponds topatient electronic medical records that contain none of the drugs in thedrug pattern of the drug-ADR rule, a first column of the contingencytable data structure corresponds to the patient electronic medicalrecords that contain the ADR in the drug-ADR rule, and a second columnof the contingency table data structure corresponds to the patientelectronic medical records that do not contain the ADR in the drug-ADRrule.
 15. The computer program product of claim 11, wherein the computerreadable program further causes the computing device to filter, by aconfounder filter logic module of the framework, the one or moredrug-ADR rules to remove a subset of one or more drug-ADR rules havingconfounder drugs specified in the drug-ADR rules of the subset, andthereby generate a filtered set of drug-ADR rules, wherein the causalmodel is generated based on the filtered subset.
 16. The computerprogram product of claim 15, wherein each drug-ADR rule in the one ormore drug-ADR rules specifies a corresponding drug pattern comprising aplurality of drugs, and corresponding adverse drug reaction, and whereinthe computer readable program further causes the computing device tofilter the one or more drug-ADR rules at least by: calculating, for eachfirst drug-ADR rule, an improvement metric specifying an amount ofimprovement of a corresponding association score for the first drug-ADRrule over an association score for another second drug-ADR rulespecifying a sub-pattern of the corresponding drug pattern of the firstdrug-ADR rule, and the corresponding adverse drug reaction of the firstdrug-ADR rule; and determining, for each first drug-ADR rule, whether tomaintain the first drug-ADR rule or remove the first drug-ADR rule basedon a value of the improvement metric.
 17. The computer program productof claim 16, wherein the computer readable program further causes thecomputing device to determine, for each drug-ADR rule, whether tomaintain the first drug-ADR rule or remove the first drug-ADR rule basedon a value of the improvement metric at least by: comparing theimprovement metric corresponding to the first drug-ADR rule to animprovement metric threshold value; in response to the improvementmetric corresponding to the first drug-ADR rule not being equal to orgreater than the improvement metric threshold value, determining that aconfounder drug is present in the corresponding drug pattern of thefirst drug-ADR rule; and identifying the confounder drug in thecorresponding drug pattern based on a difference between thecorresponding drug pattern and the sub-pattern.
 18. The computer programproduct of claim 11, wherein the computer readable program furthercauses the computing device to execute the causal model with thecognitive computing system at least by: evaluating the other patient EMRdata by applying the causal model to drug history data present in theother patient EMR data to identify probabilities of a patientencountering one or more ADRs in a set of ADRs; and generating, for thepatient, a patient model based on the identified probabilities of thepatient encountering one or more ADRs in the set of ADRs.
 19. Thecomputer program product of claim 18, wherein the computer readableprogram further causes the computing device to: input the patient modelinto the cognitive computing system which implements the patient modelto perform a cognitive operation based on the patient model and theprediction of the patient ADR for the patient, wherein the cognitiveoperation is a treatment recommendation operation that provides anoutput of a treatment recommendation to a medical practitioner based onan evaluation of the other patient EMR data by the cognitive computingsystem, and the patient model.
 20. An apparatus comprising: at least oneprocessor; and at least one memory coupled to the at least oneprocessor, wherein the at least one memory comprises instructions which,when executed by the at least one processor, cause the at least oneprocessor to implement a framework for learning multiple drug-adversedrug reaction associations, which operates to: analyze, by aco-occurrence logic module of the framework, real world evidencecomprising patient electronic medical record (EMR) data and ADR data, toidentify co-occurrences of references to drugs with references toadverse drug reactions (ADRs) to thereby generate one or more drug-ADRrules specifying one or more drug-ADR relationships; generate, by acausal association logic module of the framework, a causal model basedon the one or more drug-ADR rules, wherein the causal model comprises,for each ADR in a set of ADRs, a corresponding set of one or more rules,each rule specifying one or more drugs having a causal relationship withthe ADR; and execute the causal model with a cognitive computing systemwhich processes other patient EMR data by applying the one or moredrug-ADR rules in the causal model to generate a prediction of a patientADR for a patient.
 21. The method of claim 5, wherein filtering the oneor more drug-ADR rules comprises: calculating, for each drug-ADR rule,an association score metric that measures a strength of associationbetween a drug pattern represented in the drug-ADR rule and an ADRrepresented in the candidate rule; and filtering the one or moredrug-ADR rules based on the association score metric.